#include "tensorflow/compiler/mlir/lite/common/tfl_pass_config.h"
#include "tensorflow/compiler/mlir/lite/debug/debug.h"
#include "tensorflow/compiler/mlir/lite/flatbuffer_export.h"
#include "tensorflow/compiler/mlir/lite/flatbuffer_import.h"
#include "tensorflow/compiler/mlir/lite/ir/tfl_ops.h"
#include "tensorflow/compiler/mlir/lite/schema/schema_generated.h"
#include "tensorflow/compiler/mlir/lite/tf_tfl_passes.h"

#include "tensorflow/compiler/mlir/lite/schema/schema_generated.h"
#include "tensorflow/lite/core/model_builder.h"
#include "tensorflow/lite/tools/optimize/reduced_precision_support.h"

namespace mlir {
namespace lite {
namespace {


TEST(SparsifyModelTest, MetadataIsAddedToOutputModel) {
  std::string expected_key = tflite::optimize::kTfLiteReducedPrecisionKey;

#include "absl/container/flat_hash_set.h"
#include "flatbuffers/flatbuffer_builder.h"  // from @flatbuffers
#include "tensorflow/compiler/mlir/lite/schema/schema_generated.h"
#include "tensorflow/lite/c/c_api_types.h"
#include "tensorflow/lite/model.h"

namespace mlir {
namespace lite {

// Supported resulting types from quantization process.
enum class BufferType { QUANTIZED_INT8, QUANTIZED_FLOAT16 };

#include "mlir/Parser/Parser.h"  // from @llvm-project
#include "mlir/Support/FileUtilities.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/lite/flatbuffer_export.h"
#include "tensorflow/compiler/mlir/lite/flatbuffer_import.h"
#include "tensorflow/compiler/mlir/lite/ir/tfl_ops.h"

namespace mlir {
namespace TFL {
namespace tac {

absl::StatusOr<mlir::OwningOpRef<mlir::ModuleOp>> ImportFlatbufferOrMlir(

	z.mode = RoundingMode((b >> 5) & 7)
	z.acc = Accuracy((b>>3)&3) - 1
	z.form = form((b >> 1) & 3)
	z.neg = b&1 != 0
	z.prec = byteorder.BeUint32(buf[2:])

	if z.form == finite {
		if len(buf) < 10 {
			return errors.New("Float.GobDecode: buffer too small for finite form float")
		}
		z.exp = int32(byteorder.BeUint32(buf[6:]))
		z.mant = z.mant.setBytes(buf[10:])
	}

	if oldPrec != 0 {
		z.mode = oldMode

    case tflite::TensorType_FLOAT16:
      return builder.getF16Type();
    case tflite::TensorType_BFLOAT16:
      return builder.getBF16Type();
    case tflite::TensorType_FLOAT32:
      return builder.getF32Type();
    case tflite::TensorType_FLOAT64:
      return builder.getF64Type();
    case tflite::TensorType_INT32:
      return builder.getIntegerType(32);
    case tflite::TensorType_UINT16:

#include "stablehlo/dialect/StablehloOps.h"  // from @stablehlo
#include "tensorflow/compiler/mlir/lite/ir/tfl_ops.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_dialect.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"

namespace mlir {
namespace TFL {
namespace {
#define GEN_PASS_DEF_LEGALIZEJAXRANDOMPASS
#include "tensorflow/compiler/mlir/lite/transforms/passes.h.inc"

struct LegalizeJaxRandomPass

#include "tensorflow/compiler/mlir/lite/common/tfl_pass_config.h"
#include "tensorflow/compiler/mlir/lite/flatbuffer_export.h"
#include "tensorflow/compiler/mlir/lite/flatbuffer_import.h"
#include "tensorflow/compiler/mlir/lite/schema/schema_generated.h"
#include "tensorflow/compiler/mlir/lite/tf_tfl_passes.h"
#include "tensorflow/compiler/mlir/lite/transforms/passes.h"

    return -1;
  }

  // Set num of string
  //
  // NOTE: The string buffer is accessed here as if it's native endian (instead
  // of small endian, as documented in the header). This will protentially break
  // when TFLite is ported to big endian platforms.
  // TODO(b/165919229): This code will need changing if/when we port to a
  // big-endian platform.
  memcpy(*buffer, &num_strings, sizeof(int32_t));

  // Set offset of strings.

#include "llvm/Support/raw_ostream.h"
#include "tensorflow/compiler/mlir/lite/schema/schema_generated.h"
#include "tensorflow/compiler/mlir/lite/schema/schema_utils.h"
#include "tensorflow/lite/model.h"

using llvm::cl::opt;

// RUN: flatbuffer_translate -mlir-to-tflite-flatbuffer %s.mlir -o - \
// RUN:   | %p/importer_test_min_max - \
// RUN:   | flatbuffer_translate --tflite-flatbuffer-to-mlir - -o - \
// RUN:   | FileCheck %s